These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
Manuscript.pdf (1.57 MB)

Machine Learning for Acute Toxicity Prediction Using High-Throughput Enzyme-Reaction Chip

revised on 04.03.2019, 12:19 and posted on 04.03.2019, 16:45 by Qiannan Duan, Jianchao Lee, Jinhong Gao, Jiayuan Chen, Yachao Lian, Zoudi Wang, Can Wang, Zhaoyi Xu, Juan Ren, Sifan Bi

Machine learning (ML) has brought significant technological innovations in many fields, but it has not been widely embraced by most researchers of natural sciences to date. Traditional understanding and promotion of chemical analysis cannot meet the definition and requirement of big data for running of ML. Over the years, we focused on building a more versatile and low-cost approach to the acquisition of copious amounts of data containing in a chemical reaction. The generated data meet exclusively the thirst of ML when swimming in the vast space of chemical effect. As proof in this study, we carried out a case for acute toxicity test throughout the whole routine, from model building, chip preparation, data collection, and ML training. Such a strategy will probably play an important role in connecting ML with much research in natural science in the future.


This work is supported by the National Natural Science Foundation of China (No.50309011) and the Scientific Research Foundation for the Returned Overseas Chinese Scholars (08501041585).


Email Address of Submitting Author


School of the Environment, Nanjing University



ORCID For Submitting Author


Declaration of Conflict of Interest

The authors declare no competing interests.